Monthly Archives: August 2020

Common Econ Critiques

Consider this critique of physics:

Once upon a time the universe was full of magic, mystery, and majesty, wherein humans lived organically and intuitively with nature. But then physicists (and their engineering minions) pretended to know far more than humans can ever know in an irreducibly complex universe. And they pretended to far more objectivity and neutrality in their inquiries than is possible for humans. Using impressive math, physicists rose in status, while other less mathy but more fluid and organic ways of thinking fell in status. Physics concepts became used more widely, displacing other useful and more human but now neglected ways of thinking.

Physicists are reductionist, and focus overwhelmingly on the simplest physical parameters of the smallest physical parts. So they ignore more interesting parameters and large scale organization. They study particular phenomena via vastly-over-simplified models that neglect most of the rich complexity of the real world. Worse, regarding the items that they do consider in their simple models, most of their assumptions are just wrong.

For example, standard models of mechanical systems assume that they sit in a flat space-time. Most materials are uniform, isotropic, solid with sharp boundaries, and uncharged. Ground anchors do not rotate or accelerate. Perfect vacuum sits between most adjacent parts, and between the other pairs is either an absolute bond or frictionless relative motion. Yet real mechanical systems sit in rotating, accelerating environments full of corrosive fluids and cosmic rays, at temperatures and pressures that often melt materials, and amid vibrations that often break them. And estimates of all physical parameters in such models are known to be wrong, i.e., not exactly correct. Physicists claim that such deviations make for only small errors in their final analysis, but how can they know that if they don’t model the full complexity?

Engineers who use physics tend to create system designs that are more like typical physics models, with a small number of simple parts having a few simple relations to one another. These systems are quite different from the fluid, complex, highly-interdependent rich-relation biological systems that we are, and once lived among. These physics-model-derived systems are harsh, ugly, fragile, uninspiring, and alienating. These systems may do well by simple physics metrics, but that neglects a vast space of better if less formal ways to evaluate systems.

The dominance of physics in engineer training and related government policy has unfairly neglected intuitive, magical, arty, and literary approaches to engineering system design. Approaches that look bad by physics metrics, but not by intuitive organic human ways to evaluate. Today the fields of “design” use better approaches, and are displacing the fields of “engineering”. It’s about time.

Here’s an obvious response:

For most products, few customers care much about how their systems are engineered, or the parameters by which they are described. So in a free competitive world, firms are free to offer products designed and evaluated via “intuitive, magical, arty, and literary approaches.” But few do. Yes, firms today also commonly use design as well as engineering, but mainly for a few relatively aesthetic choices close to the user experience. For at the vast majority of other choices, out of user sight, physics-based engineering dominates.

Physics winning this competition suggests that alternate approaches just aren’t as productive. Yes, there is often less free competition to woo government buyers, and physics-dominated regulations often demand that physics be used to prove that products are safe and effective. But consider that the world still has many competing nations, and engineering matters greatly in war, where simple physical parameters are quite meaningful. If a nation could build more effective weapons using other approaches to weapons design, they could win wars that way. The fact that few nations try is more evidence that physics-based approaches work better.

Yes, models greatly simplify. But for humans with some abstract understanding and greatly limited mental abilities of other sorts, approximation via simple modular models and designs is our main way to manage complexity. Nature faced different constraints, which is why her designs are different. Yes, simple modular designs can be harsh and alienating, but without them we could not create engineering designs nearly as capable. Humans just can’t do analysis without making a mass of simplifying, and thus wrong, assumptions. But the fact that our designs tend to work shows that our approximations tend to be appropriate. Yes of courses if we approximate badly, our models and designs will go badly. Which is why physicists and engineers pay so much attention to approximating well.

Now consider the many critiques of economics, which I’ve just spent many hours sampling. Most econ critiques are much like the above physics critique, making a similar response appropriate. But with one key difference, to be discussed at the end.

Before going into details, let’s review a few basics. Like physics, econ uses math to create a space of possible models. But instead of describing physical systems, econ models describe social systems. Economists have a standard set of assumptions that they see as most likely to be true, and other standard set of assumptions that seem easiest to analyze. Assumptions from the second set are often preferred, to allow entire models to be simple enough to analyze. Different economists explore different models, comparing them to each other and to data, and arguing about their relative accuracy as approximations. If you are arguing for different models in this topic area, but accepting that models are a reasonable way to think about social behavior, then you are doing econ. (And you might have a valid complaint re if your kind of econ gets a fair hearing.) Econ critics, in contrast, reject, or at lest minimize the value of, the whole econ approach to studying social behavior, and designing policy.

That said, let us now consider some common econ criticisms. Continue reading "Common Econ Critiques" »

GD Star Rating
loading...
Tagged as: ,

How To Pick A Quack: Data

How do we pick, or think we should pick, our experts? One clue comes from “How to pick an X” web guides. For 18 types of experts X, I searched for that phrase, and read the top 8 Google hits, noting all of the types of info clues mentioned in each guide. Here is the full table of results.

Here are the 25 most common clue types, sorted by the % of these guides in which each is mentioned:

Here are the 18 types of experts, sorted by the average number of clue types that their guides mention:

Looking at these tables, I hypothesized that guides might prefer to mention types of clues that we’d more want to use in making our choices, and that guides might mention more clues for kinds of experts where we worry more about choosing them well. So I’ve done a set of 16 Twitter polls to estimate these things for 16 types of experts and 16 type of clues.

Results to note:

  • Guides for 18 different types of experts vary by a factor of 3 in how many types of clues they mention.
  • The top 25 info clues vary by a factor of 12 in how often they are mentioned in guides.
  • While different clues are favored in guides for different types of experts, the overall pattern looks pretty random.
  • Only 7.8% of guides mention a top 25 clue directly sensitive to outcomes. (Ones marked in red above.)
  • The correlation between how many clues guides to X mention and how worried poll respondents are re pick X is strong: +0.40.
  • The correlation between how often guides mention a clue and how much poll respondents want to know it to pick is negative: -0.20. This is mainly because polls put the most weight on track records. My followers are probably less representative here, as that’s an issue I talk much about.

Guides do not often mention outcome-related clues, presumably as few customers attend to them. In general, we can’t tell if a type of expert X is a “quack”, where “better” versions don’t help customers much more with outcomes, by the kind of clues people use to pick X. Maybe most people can’t tell the difference.

So what explanations can you offer for any of the patterns you see?

Added: Here are the poll-based priorities each expert type and info clue: Continue reading "How To Pick A Quack: Data" »

GD Star Rating
loading...
Tagged as: ,

Russell’s Human Compatible

My school turned back on its mail system as we start a new semester, and a few days ago out popped Stuart Russell’s book Human Compatible (published last Oct.), with a note inside dated March 31. Here’s my review, a bit late as a result.

Let me focus first on what I see as its core thesis, and then discuss less central claims.

Russell seems to say that we still have a lot of time, and that he’s only asking for a few people to look into the problem:

The arrival of super intelligence AI is inherently unpredictable. … My timeline of, say eighty years is considerably more conservative than that of the typical AI researcher. … If just one conceptual breakthrough were needed, …superintelligent AI in some form could arrive quite suddenly. The chances are that we would be unprepared: if we built superintelligent machines with any degree of autonomy, we would soon find ourselves unable to control them. I’m, however, fairly confident that wee have some breathing space because there are several major breakthroughs needed between here and superintelligence, not just one. (pp.77-78)

Scott Alexander … summed it up brilliantly: … The skeptic’s position seems to be that, although we should probably get a couple of bright people to start working on preliminary aspects of the problem, we shouldn’t panic or start trying to ban AI research. The “believers,” meanwhile [take exactly the same position.] (pp.169-170)

Yet his ask is actually much larger: unless we all want to die, AI and related disciplines must soon adopt a huge and expensive change to their standard approach: we must stop optimizing using simple fixed objectives, like the way a GPS tries to minimize travel time, or a trading program tries to maximize profits. Instead we must make systems that attempt to look at all the data on what all humans have ever done to infer a complex continually-updated integrated representation of all human preferences (and meta-preferences) over everything, and use that complex representation to make all automated decisions. Modularity be damned: Continue reading "Russell’s Human Compatible" »

GD Star Rating
loading...
Tagged as: , ,

Lognormal Priorities

In many polls on continuous variables over the last year, I’ve seen lognormal distributions typically fit poll responses well. And of course lognormals are also one of the most common distributions in nature. So let’s consider the possibility that, regarding problem areas like global warming, falling fertility, or nuclear war, distributions of priority estimate are lognormal.

Here are parameter values (M = median, A = (mean) average, S = sigma) for lognormal fits to polls on how many full-time equivalent workers should be working on each of the following six problems:

Note that priorities as set by medians are quite different from those set by averages.

Imagine that someone is asked to estimate their (median) priority of a topic area. If their estimate results from taking the product of many estimates regarding relevant factors, then not-fully-dependent noise across different factors will tend to produce a lognormal distribution regarding overall (median) estimates. If they were to then act on those estimates, such as for a poll or choosing to devote time or money, we should see a lognormal distribution of opinions and efforts. When variance (and sigma) is high, and effort is on average roughly proportional to perceived priority, then most effort should come from a quite small fraction of the population. And poll answers should look lognormal. We see both these things.

Now let’s make our theory a bit more complex. Assume that people see not only their own estimate, but sometimes also estimates of others. They then naturally put info weight on others’ estimates. This results in a distribution of (median) opinions with the same median, but a lower variance (and sigma). If they were fully rational and fully aware of each others’ opinions, this variance would fall to zero. But it doesn’t; people in general don’t listen to each other as much as they should if they cared only about accuracy. So the poll response variance we see is probably smaller than the variance in initial individual estimates, though we don’t know how much smaller.

What if the topic area in question has many subareas, and each person gives an estimate that applies to a random subarea of the total area? For example, when estimating the priority of depression, each person may draw conclusions by looking at the depressed people around them. In this case, the distribution of estimates reflects not only the variance of noisy clues, but also the real variance of priority within the overall area. Here fully rational people would come to agree on both a median and a variance, a variance reflecting the distribution of priority within this area. This true variance would be less than the variance in poll responses in a population that does not listen to each other as much as they should.

(The same applies to the variance within each person’s estimate distribution. Even if all info is aggregated, if this distribution has a remaining variance, that is “real” variance that should count, just as variance within an area should count. It is the variance resulting from failing to aggregate info that should not count.)

Now let’s consider what this all implies for action biases. If the variance in opinion expressed and acted on were due entirely to people randomly sampling from the actual variance within each area, then efforts toward each area would end up being in proportion to an info-aggregated best estimates of each area’s priority – a social optimum! But the more that variance in opinion and thus effort is also due to variance in individual noisy estimates, then the more that such variance will distort efforts. Efforts will go more as the average of each distribution, rather than its median. The priority areas with higher variance in individual noise will get too much effort, relative to areas with lower variance.

Of course there are other relevant factors that determine efforts, besides these priorities. Some priority areas have organizations that help to coordinate related efforts, thus reducing free riding problems. Some areas become fashionable, giving people extra social reasons to put in visible efforts. And other areas look weird or evil, discouraging visible efforts. Even so, we should worry that too much effort will go to areas with high variance in priority estimate noise. All else equal, you should avoid such areas. Unless estimate variance reflects mostly true variance within an area, prefer high medians over high averages.

Added 3p: I tried 7 more mundane issues, to see how they varied in variance. The following includes all 13, sorted by median.

GD Star Rating
loading...
Tagged as: , ,

Sim Argument Confidence

Nick Bostrom once argued that you must choose between three options re the possibility that you are now actually living in and experiencing a simulation created by future folks to explore their past: (A) its true, you are most likely a sim person living in a sim, either of this sort or another, (B) future folk will never be able to do this, because it just isn’t possible, they die first, or they never get rich and able enough, or (C) future folk can do this, but they do not choose to do it much, so that most people experiencing a world like yours are real humans now, not future sim people.

This argument seems very solid to me: future folks either do it, can’t do it, or choose not to. If you ask folks to pick from these options you get a simple pattern of responses:

Here we see 40% in denial, hoping for another option, and the others about equally divided among the three options. But if you ask people to estimate the chances of each option, a different picture emerges. Lognormal distributions (which ignore the fact that chances can’t exceed 100%) are decent fits to these distributions, and here are their medians:

So when we look at the people who are most confident that each option is wrong, we see a very different picture. Their strongest confidence, by far, is that they can’t possibly be living in a sim, and their weakest confidence, by a large margin, is that the future will be able to create sims. So if we go by confidence, poll respondents’ favored answer is that the future will either die soon or never grow beyond limited abilities, or that sims are just impossible.

My answer is that the future mostly won’t choose to sim us:

I doubt I’m living in a simulation, because I doubt the future is that interested in simulating us; we spend very little time today doing any sort of simulation of typical farming or forager-era folks, for example. (More)

If our descendants become better adapted to their new environment, they are likely to evolve to become rather different from us, so that they spend much less of their income on sim-like stories and games, and what sims they do like should be overwhelmingly of creatures much like them, which we just aren’t. Furthermore, if such creatures have near subsistence income, and if a fully conscious sim creature costs nearly as much to support as future creatures cost, entertainment sims containing fully conscious folks should be rather rare. (More)

If we look at all the ways that we today try to simulate our past, such as in stories and games, our interest in sims of particular historical places and times fades quickly with our cultural distance from them, and especially with declining influence over our culture. We are especially interested in Ancient Greece, Rome, China, and Egypt, because those places were most like us and most influenced us. But even so, we consume very few stories and games about those eras. And regarding all the other ancient cultures even less connected to us, we show far less interest.

As we look back further in time, we can track decline in both world population, and in our interest in stories and games about those eras. During the farming era population declined by about a factor of two every millennium, but it seems to me that our interest in stories and games of those eras declines much faster. There’s far less than half as much interest in 500AD than in 1500AD, and that fact continues for each 1000 year step backward.

So even if future folk make many sims of their ancestors, people like us probably aren’t often included. Unless perhaps we happen to be especially interesting.

GD Star Rating
loading...
Tagged as: , ,

Lost Advanced Civilizations

Did life on Earth start on Earth, or did it start on Mars and move to Earth? If you frame such panspermia as an “extraordinary claim” for which you demand “extraordinary evidence”, you will of course conclude that this should be treated “skeptically” as unlikely and sloppy unscientific “speculation”. To be disdained and not treated as serious by respectable academics and science journalists. But that’s not really fair.

You see the early Mars environment is, a priori, about as likely a place for life to start as the Earth environment. So if the rate at which life is transferred between the planets were high enough, then equal chances of life starting first in both places would result in equal chances for Earth life to have started in either place. We should take the expected time difference between life starting in the two places, and ask how high is the chance that life would move from one planet to the next during that period. The more often rocks are thrown from one place to the other, and the more easily life could survive for the travel period within those rocks, then the more likely it is that Earth life started on Mars.

In addition, Mars, being further from the Sun, would have cooled first, and had a head start in its window for life. Making it more likely that life would start there and spread to Earth than vice versa. Of course life starting first on Mars would have implications for what we might see when we look at Mars. If we had expected Mars life to continue strong until today, then the fact that we see no life on Mars now would be a big strike against this hypothesis. But if we expected Mars life to have died out or at least gone dormant by now, then the issue is what we will see when we dig on Mars. With enough data on such digs, we may come to reject to Mars first hypothesis even given its initial plausibility.

A similar analysis applies to panspermia from other stars. You might think it obvious that the rate at which life-filled rocks from a star make it to seed other stars is very low, but most stars are born in large groups close together in stellar nurseries. So if life arose early enough within our star’s nursery, there might have been high rates of moving that life between stars in that nursery. In which case the chance that Earth life came from another star could also be high, and the best place to look for life outside our star would be the other stars from our stellar nursery.

Now consider the possibility of lost advanced civilizations. Not just civilizations at a similar level of development to those around them in space and time; that’s quite likely given that we keep finding new previously-unknown settlements and developed places. No, the more interesting claims are about substantial (but not crazy extreme) decreases in the peak or median level of civilizations across wide areas. Such as what happened late in the late Mediterranean Bronze Age, or at the fall of the Roman Empire. Could there have been “higher” civilizations before the “first” ones that we now know about in each region, such as the Sumerians, Egyptians, and Chinese Shang dynasty? (I’m talking human civs, not others.) Continue reading "Lost Advanced Civilizations" »

GD Star Rating
loading...
Tagged as: , , , ,

Breadth, Humor Show Privilege

Imagine an old costume drama, showing servants interacting with those they serve. Think about what topics each side is allowed to mention, and how much humor would be tolerated from each. I say it is obvious that there are far fewer topics on which the servants may speak, or if they may speak, may joke. If there is a way of seeing what a servant said as purposely rude or malicious, they will be not be given much benefit of the doubt or chance to argue that they’ve been misunderstood. You must be very careful re first impressions when a second impression is unlikely to follow a bad first one.

Now imagine conversations centuries ago between English and French. Who is allowed to talk or joke about topics related to English-French conflicts, or ways in which the two groups are said to differ? If the conversation is taking place in an English context, with far more English than French present, then unless the French are especially high status or focal to the event, my guess is that the French will have to be more careful about what they say. The French are more at risk of harm here if they are accused of insulting the English, than vice versa.

Over the last few years, I’ve been told many times that there are many (and increasingly more) topics on which I, as a older white cis male presumed-conservative STEM-associated economist, must not speak. Often not even to directly quote others who may speak. And if I may speak, I must not joke. These categories of mine make me presumed evil, I am told. So if I say any combination of words where, taken out of context, it is possible to interpret them as “dog-whistling” an evil intent, observers are said to be free to treat that as my actual intent. Language being as ambiguous as it is, it is hard to talk long on any topic without such combinations appearing occasionally. And as humor relies much more on ambiguity and exaggeration, humor greatly increases their frequency.

When people separate the world into “us” and “them”, and try to explain why “we” are better, they often say that “we” are more honest, knowledgeable, and open-minded, and so are more willing and able to discuss a wider range of topics. And they often say that “they” are stupid dull humorless inhuman drones, lacking key sparks of life, such as humor, wit, curiosity, and spontaneity. But you can see from the above analysis that this impression is often misleading. In contexts where you hold the upper hand, they will more often have to limit their topics and humor, to avoid your wrath.

GD Star Rating
loading...
Tagged as: ,

Dominance Explains Paternalism

My Ph.D. is in formal political theory, but I’ve come to realize that it is usually best to think of political behavior not as some different kind of thing, but instead as an extension of or variation on ordinary behavior. This seems to me especially true for paternalism, which I’ve spend much effort pondering. I did a game theory analysis of it for my job talk long ago, and Bryan Caplan just reviewed what seems to be a nice book puzzling over “behavioral” explanations. But on reflection a key explanation seems pretty simple.

In our personal lives, we all know that some of the people around us are more “control freaks”; they push harder for control over what they and their associates do. First they push to control their own lives, then they push for more control of shared context and choices, like which restaurant a group goes to, and finally they push for control over the lives of others. Such as by nagging and berating others re what to eat or wear, or with whom to associate. Or by becoming official leaders and authorities, with formal power to make people do what they say.

I just did two polls that say that most of us think that this control freak pressure tends to hurt associates, and also that control freaks tend more to be “do-gooders”, who talk more about making the world better, and more give that rationale for things they do:

Dominance seems to me the obvious interpretation here. Like most animals, humans strive to dominate each other, in order to rise in the local “pecking order”. And control over ourselves and others not only brings many direct benefits, it is widely taken as one of the strongest signs of dominance and non-submission. But unlike other animals, humans have norms against overt dominance and submission, and norms promoting pro-social behavior, that helps others. So we do push to dominate, but we pretend that we are actually just trying to help. And as usual, we are typically not consciously aware of our hypocrisy. In our mind, we are mainly aware of how they are doing the wrong things, and how they would be so much better off if only we could make them do things our way.

It is not just individuals who try to dominate to gain status; groups coordinate to dominate together as well. For example, parents coordinate to dominate their kids. So we push for our groups to have autonomy, and also control over other groups. And so in politics, where our main motive is to show loyalty to our allies, we each push for our political coalitions to have more self-control, and more control over other groups. So when there is an option for “regulators” or other authorities to take more control over ordinary lives, we tend to support that when we see those authorities as part of our coalition, and those “helped” as part of rival coalitions. Else we may resist.

Of course we actually do often need leaders to make central decisions that effect many others. And people do sometimes make bad decisions that can be improved via pressures from others around them. So dominance isn’t the only cause of leadership or paternalism. This is another example of a key principle: people can only successfully pretend to have motive X to cover real motive Y if sometimes X really is a substantial motive. “The dog ate my homework” works better as an excuse than “The dragon ate my homework.” For a cover to work, it has to be sufficiently plausible. So all the motives we pretend to have really do apply to some people at some times; just not nearly as often as we suggest.

So the claim is not that paternalism or dominant leaders can never be appropriate. Instead, the claim is that there’s a strong tendency to try to justify other more selfish and harmful behaviors via such needs. So we need to hold a much higher standard on leadership than “we should do whatever leaders say because we need leaders.” And we need to hold a higher standard on paternalism than “you should do what regulators say because they are authorities.” Leaders and authorities should be accountable to make their choices actually help via more than a mere dominance struggle for power to grab such positions.

In small firms, leaders are often given rewards that depend on the overall success of those firms. And subordinates who feel they are treated badly may well leave. Together, these can greatly temper leader temptations to use powers of their dominant positions to seek to gain status over their subordinates, relative to actually helping their groups. And in the distant past, in small groups within very war-like areas, dominant leaders faced related outside threats of military competition, and of subordinates running away to other nearby areas.

But today in large mostly-peaceful nations, political leaders tend to lack these other disciplines to temper their tyranny. Which is why it becomes so important today to find other ways to hold political leaders and authorities accountable, to limit their arbitrary dominance. Such as via elections, law, and property rights. I’ve tried to explore new methods, such as futarchy and vouching. But until they are fielded we should keep the old ways, and hold our leaders and authorities to much higher standards than “because I said so”.

In our society today, paternalistic authorities often claim that they are disciplined not so much by profit, voters, or law, but by “science”. You see, they only make people do things when “science” says that is for the best. Having seen how such “science” actually works in these contexts, I’m relatively skeptical of this as an effective discipline today. Too often, this is just a way to justify applying the widespread opinions of social classes and coalitions with which regulators ally.

Added 1p: Teaching kids to play a musical instrument is a striking example of paternalism. Even though data doesn’t suggest that it improves discipline or other academic performance, many passionately want to force this on not only their own kids, but also the kids of others, even those who feel strongly that they don’t want to play. Though most adults enjoy listening to music, few of them choose to play instruments, especially among those who were forced.

Yet people argue that we must force all kids to play so that they can enjoy music as adults and be more attractive as mates, or so that we can find the few good musicians, or so that we can increase the supply of music. Which seem pretty laughable arguments. More plausibly people identify with musicians and cultures that respect them, and so want to force others to respect them as well, especially kids whose status contributes to their own personal status.

GD Star Rating
loading...
Tagged as: , ,

Remote Work Specializes

We seem on track to spend far more preventing pandemic health harm than we will suffer from it, which seems too much spending given the apparent low elasticity of harm w.r.t. prevention. But an upside is that some of this prevention effort is being invested in remote work, which is helping to develop and innovate such capacities. Which matters because remote work (a.k.a. telecommuting) is my guess for the most important neglected trend over the next 30 years. (At least of trends we can foresee now.)

My recent polls put remote work at #24 out of 44 future trends, which IMHO greatly underrates it. AGI, biotech, crypto, space, and quantum computing are far overrated (due to drama & status). Automation matters but will continue steadily as it has for many decades, not causing much trend deviation. Global warming, non-carbon energy, the rise of Asia, falling fertility, and the rise of cybersecurity and privacy are important trends, but their trend deviation implications tend more to be correctly anticipated. However, I see remote work as big and mattering more than and driving trends in migration, aug./virtual reality, and self-driving cars. And remote work implications seem neglected and unappreciated.

Remote work has been a topic of speculation for many decades, so likely somewhere out there is an author who sees it right. But I haven’t yet found that author. I’ve recently read a dozen or so recent discussions of remote work, and all of them seem to miss the main reason that remote work will be such a big deal: specialization due to agglomeration (i.e., more interaction options). The two most formal math analyses I could find actually explicitly assume that remote work, in contrast to traditional work,  produces no agglomeration gains! In contrast, these discussions get closer to the truth: Continue reading "Remote Work Specializes" »

GD Star Rating
loading...
Tagged as: , , ,

What Future Areas Matter Most?

I made a list of 44 possibly important future areas, and just did 22 Twitter polls (with N from 379 to 1178), each time asking this question re 4 areas:

Over next 30 years, changes in which are likely to matter most?

I fit the answers to a simple model wherein respondents either pick randomly (~26% of time) or pick in proportion to each area’s (non-negative) “strength”. Here are the estimated area strengths, relative to the strongest set to 100:

Some comments:

  1. The area with the largest modeling error is migration, so politics may be messing that up.
  2. Governance mechanisms looks surprisingly strong, especially relative to its media attention.
  3. The top 7 areas hold half the total strength, and there’s a big drop to #8. ~20% is in automation, AGI, and self-driving cars.
  4. 19 areas have strengths lying within about the same factor of two. So many things seem important.
  5. Relative to these strength ratings, it seems to me that media focus is only roughly correlated. Media seems disproportionately focused on areas involving more direct social conflict.
  6. Areas add roughly linearly. For example, biotech arguably includes life extension, meat, and materials, and pandemics, and its strength is near their strength sum.
GD Star Rating
loading...
Tagged as: ,